快速序列视觉呈现任务下的脑电分类算法

In this project, we proposed a classification algorithm of electroencephalogram (EEG) signals in order to fulfill the Rapid Serial Visual Presentation (RSVP) task. Firstly, the EEG signals of the subjects were recorded when they received the image sequences and then segmented to creat a sample set. Secondly, by confining the difference between the sample and the sample center after supervised dimensionality reduction, the mapping matrix was obtained after training EEG data from the training set. EEG samples of training set and test set were transformed into feature vectors by using feature extracting function, and support vector machine (SVM) was used to classify the EEG samples. The experiment results showed that the average classification accuracy rate of EEG of 24 subjects was 91.5% and the average AUC was 0.95, which indicates that the EEG classification algorithm has good classification performance and can accurately detect target images in the Rapid Serial Visual Presentation tasks.